A Model about the Impact of Ability Grouping on Student Achievement

Kiss, David

doi:10.1515/bejeap-2017-0036

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…The comprehensive quality evaluation of college students cannot be simply measured by academic achievement but should be based on scientific evaluation theory and advanced evaluation technology to evaluate the whole process in an all-round and multidimensional way. The Markov model can mine students' data inside and outside the classroom, establish and improve college students' achievement index system, then deeply analyze and discuss the development of college students' achievement and ability, and finally give each student's achievement results [13,14].…”

Section: Introductionmentioning

confidence: 99%

Markov Model-Based Curriculum Association Classification Model and Student Achievement Prediction

Gu¹,

Deng²

2022

Wireless Communications and Mobile Computing

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With the increasing popularity of higher education and the increasing scale of students in schools, the educational administration management system of various colleges and universities has also accumulated a large amount of performance data. In the face of these massive data, many teaching managers still stay in the simple operation of adding, deleting, modifying, and checking the data and cannot effectively extract and analyze the useful knowledge and information hidden behind the data. Therefore, Markov model is proposed. The advantage of the Markov model is that it has better prediction effect on random series and data series with large volatility; that is, grey prediction model is used to reveal the overall trend of development and change of prediction data series. This paper studies the curriculum association classification model and student achievement prediction based on the Markov model. According to the student’s historical achievement, the average missed detection rate of future grade point is 48.65%, the average missed detection rate of Apriori algorithm is 35.5%, the average missed detection rate of FP growth algorithm is 43.2%, the average missed detection rate of this algorithm is 37.5%, and only 17 of the 40 grade point interval predictions match the actual interval. Make full use of the association rules between courses to provide students with early warning and teacher guidance for specific courses, which can effectively reduce the failure rate while reducing the academic burden. The Markov model can mine students’ data inside and outside the classroom, establish and improve college students’ achievement index system, then deeply analyze and discuss the development of college students’ achievement and ability, and finally give each student’s achievement results.

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Section: Introductionmentioning

confidence: 99%

Markov Model-Based Curriculum Association Classification Model and Student Achievement Prediction

Gu¹,

Deng²

2022

Wireless Communications and Mobile Computing

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Introduction – Education, Competence Development and Career Trajectories

Blossfeld

Weinert

2023

Methodology of Educational Measurement and Assessment

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A lifespan perspective on development and education and corresponding longitudinal studies have long been proposed scientifically in many disciplines. However, even in the 2000s, little was known about education as a lifelong process or about the cumulative and interactive effects of learning that takes place in different educational settings across the lifespan and comprehensive longitudinal studies were rare. The German National Educational Panel Study (NEPS) was therefore designed to provide longitudinal data on educational trajectories and competence development from infancy to old age, and to make it possible to examine inter- and intraindividual changes and pathways in relation to family, educational institutions, workplaces, private life, and individual characteristics of the target persons. The data also addresses the institutions of the German educational system such as crèches and preschools, primary and secondary schools, vocational training, tertiary education, and opportunities of further learning in adulthood. It further enables the analysis of relevant monetary and non-monetary returns to education over the life course. The chapter briefly introduces important features of the multi-cohort sequence design of the NEPS and discusses the relevance of longitudinal data. Furthermore, it outlines Germany as a special case by introducing specific features of the German education system as well as some international comparative findings. Finally, an overview of the following chapters is given.

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The Interplay Between Instructional Pace, Skill Externalities, and Student Achievement: An Empirical Assessment

Kiss

2023

Methodology of Educational Measurement and Assessment

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This article empirically validates a theoretical model from Kiss (B.E. J Econ Anal Policy 17:1–10, 2017) that addresses the transmission channels through which increases in peer achievement levels may affect other students’ achievement. In his model, a higher share of better students has two effects: first, weaker students benefit from skill externalities generated by their better classmates. At the same time, however, better students further induce teachers to instruct at a more demanding pace. Based on these two effects, I derive three hypotheses and test them on data on German secondary school students. Empirical findings are consistent with the model’s predictions: increases in the share of better classmates (a) are always beneficial for good students, (b) may hurt weak students, and (c) boost weak students’ achievement if the extent of interaction between better and weaker students is high. Taken together, these findings suggest that encouraging better and weaker students to interact more could be Pareto-improving.

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A Model about the Impact of Ability Grouping on Student Achievement

Cited by 3 publications

References 44 publications

Markov Model-Based Curriculum Association Classification Model and Student Achievement Prediction

Markov Model-Based Curriculum Association Classification Model and Student Achievement Prediction

Introduction – Education, Competence Development and Career Trajectories

The Interplay Between Instructional Pace, Skill Externalities, and Student Achievement: An Empirical Assessment

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